Hippocratic AI
Forward Deployed Engineer
About this role
Deploy and operate production AI agents embedded within healthcare systems, architecting end-to-end conversational AI solutions that integrate with clinical workflows and EHR systems. You'll own complete deployments from RAG pipeline design through production monitoring, serving as the technical expert partner to healthcare organizations scaling AI at the point of care.
What you'll do
- Design and implement RAG pipelines grounded in customer clinical data with focus on accuracy, latency, and data governance
- Build tool-calling and MCP architectures enabling AI agents to securely interact with EHRs and operational systems
- Develop production Python code using LangChain and modern LLM frameworks to solve novel healthcare AI problems
- Execute end-to-end deployments including infrastructure setup, testing, monitoring configuration, and go-live execution
- Monitor production systems, respond to incidents, troubleshoot issues, and implement reliable fixes
- Partner with healthcare customers as technical expert to explain AI capabilities, limitations, and build system confidence
What they're looking for
- Python development with strong fundamentals and production experience
- LLM frameworks (LangChain, LangSmith) and modern LLM development patterns
- RAG, prompt engineering, tool calling, LLM-as-judge, and chain-of-thought reasoning
- API integrations, database work, and enterprise system connectivity
- Async patterns, error handling, and reliability engineering
- Model Context Protocol (MCP) or similar orchestration frameworks
- EHR integration experience (Epic, Cerner, Athena, FHIR, HL7)
- DevOps, monitoring, alerting, logging, and incident response
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Hippocratic AI
Hippocratic AI builds safety-focused generative AI systems for healthcare, developing LLM-powered clinical dialogue platforms and integrations with electronic health records. The company is hiring infrastructure engineers, performance specialists, prompt engineers, and integration engineers to optimize AI inference, ensure system reliability, and connect healthcare data ecosystems.
View all jobs at Hippocratic AILikely interview questions
- Walk us through a complex RAG implementation you've built—how did you handle retrieval latency, hallucination, and data accuracy in production?
- Describe your experience integrating with enterprise systems via APIs. How do you design error handling and graceful degradation when external dependencies fail?